A Hydrogen-Fueled Micro Gas Turbine Unit for Carbon-Free Heat and Power Generation

The energy transition with transformation into predominantly renewable sources requires technology development to secure power production at all times, despite the intermittent nature of the renewables. Micro gas turbines (MGTs) are small heat and power generation units with fast startup and load-following capability and are thereby suitable backup for the future’s decentralized power generation systems. Due to MGTs’ fuel flexibility, a range of fuels from high-heat to low-heat content could be utilized, with different greenhouse gas generation. Developing micro gas turbines that can operate with carbon-free fuels will guarantee carbon-free power production with zero CO2 emission and will contribute to the alleviation of the global warming problem. In this paper, the redevelopment of a standard 100-kW micro gas turbine to run with methane/hydrogen blended fuel is presented. Enabling micro gas turbines to run with hydrogen blended fuels has been pursued by researchers for decades. The first micro gas turbine running with pure hydrogen was developed in Stavanger, Norway, and launched in May 2022. This was achieved through a collaboration between the University of Stavanger (UiS) and the German Aerospace Centre (DLR). This paper provides an overview of the project and reports the experimental results from the engine operating with methane/hydrogen blended fuel, with various hydrogen content up to 100%. During the development process, the MGT’s original combustor was replaced with an innovative design to deal with the challenges of burning hydrogen. The fuel train was replaced with a mixing unit, new fuel valves, and an additional controller that enables the required energy input to maintain the maximum power output, independent of the fuel blend specification. This paper presents the test rig setup and the preliminary results of the test campaign, which verifies the capability of the MGT unit to support intermittent renewable generation with minimum greenhouse gas production. Results from the MGT operating with blended methane/hydrogen fuel are provided in the paper. The hydrogen content varied from 50% to 100% (volume-based) and power outputs between 35kW to 100kW were tested. The modifications of the engine, mainly the new combustor, fuel train, valve settings, and controller, resulted in a stable operation of the MGT with NOx emissions below the allowed limits. Running the engine with pure hydrogen at full load has resulted in less than 25 ppm of NOx emissions, with zero carbon-based greenhouse gas production.publishedVersio

Dynamic Modeling of ORC Power Plants

This chapter presents dynamic modeling approaches suitable for organic Rankine cycle (ORC) power plants. Dynamic models are necessary for the better understanding of the plants’ behavior during transient operation, such as start-up, shutdown, and during rapid load changes. The estimation of plant operating parameters during transient operation is crucial for monitoring and control of the plant so that the system state variables do not exceed the pre-defined operating range. One example is the proportion of liquid and vapor phase in the condenser and evaporator that must be kept within acceptable ranges to avoid stalling or temperature shocks during transient conditions. Using dynamic models enables plant operators to predict changes in power output as a function of the plant’s boundary conditions such as temperature of the heat source and ambient conditions, so that they can respond to the expected heat and power demand accordingly. The aim of the chapter is to investigate and review the methodologies applicable for dynamic simulation of ORC power plants

Petroleum Sector-Driven Roadmap for Future Hydrogen Economy

In the climate change mitigation context based on the blue hydrogen concept, a narrative frame is presented in this paper to build the argument for solving the energy trilemma, which is the possibility of job loss and stranded asset accumulation with a sustainable energy solution in gas- and oil-rich regions, especially for the Persian Gulf region. To this aim, scientific evidence and multidimensional feasibility analysis have been employed for making the narrative around hydrogen clear in public and policy discourse so that choices towards acceleration of efforts can begin for paving the way for the future hydrogen economy and society. This can come from natural gas and petroleum-related skills, technologies, experience, and infrastructure. In this way, we present results using multidimensional feasibility analysis through STEEP and give examples of oil- and gas-producing countries to lead the transition action along the line of hydrogen-based economy in order to make quick moves towards cost effectiveness and sustainability through international cooperation. Lastly, this article presents a viewpoint for some regional geopolitical cooperation building but needs a more full-scale assessment.publishedVersio

Can methane pyrolysis based hydrogen production lead to the decarbonisation of iron and steel industry ?

Decarbonisation of the iron and steel industry would require the use of innovative low-carbon production technologies. Use of 100% hydrogen in a shaft furnace (SF) to reduce iron ore has the potential to reduce emissions from iron and steel production significantly. In this work, results from the techno-economic assessment of a H2-SF connected to an electric arc furnace(EAF) for steel production are presented under two scenarios. In the first scenario H2 is produced from molten metal methane pyrolysis in an electrically heated liquid metal bubble column reactor. Grid connected low-temperature alkaline electrolyser was considered for H2 production in the second scenario. In both cases, 59.25 kgH2 was required for the production of one ton of liquid steel (tls). The specific energy consumption (SEC) for the methane pyrolysis based system was found to be 5.16 MWh/tls. The system used 1.51 MWh/tls of electricity, and required 263 kg/tls of methane, corresponding to an energy consumption of 3.65 MWh/tls. The water electrolysis based system consumed 3.96 MWh/tls of electricity, at an electrolyser efficiency of 50 KWh/kgH2. Both systems have direct emissions of 129.4 kgCO2/tls. The indirect emissions are dependent on the source of natural gas, pellet making process and the grid-emission factor. Indirect emissions for the electrolysis based system could be negligible, if the electricity is generated from renewable energy sources. The levellized cost of production(LCOP) was found to be $631, and$669 respectively at a discount rate of 8%, for a plant-life of 20 years. The LCOP of a natural gas reforming based direct reduction steelmaking plant of operating under similar conditions was found to be $414. Uncertainty analysis was conducted for the NPV and IRR values.publishedVersio

Techno-economic assessment of hydrogen production from seawater

Population growth and the expansion of industries have increased energy demand and the use of fossil fuels as an energy source, resulting in release of greenhouse gases (GHG) and increased air pollution. Countries are therefore looking for alternatives to fossil fuels for energy generation. Using hydrogen as an energy carrier is one of the most promising alternatives to replace fossil fuels in electricity generation. It is therefore essential to know how hydrogen is produced. Hydrogen can be produced by splitting the water molecules in an electrolyser, using the abondand water resources, which are covering around ⅔ of the Earth's surface. Electrolysers, however, require high-quality water, with conductivity in the range of 0.1–1 μS/cm. In January 2018, there were 184 offshore oil and gas rigs in the North Sea which may be excellent sites for hydrogen production from seawater. The hydrogen production process reported in this paper is based on a proton exchange membrane (PEM) electrolyser with an input flow rate of 300 L/h. A financially optimal system for producing demineralized water from seawater, with conductivity in the range of 0.1–1 μS/cm as the input for electrolyser, by WAVE (Water Application Value Engine) design software was studied. The costs of producing hydrogen using the optimised system was calculated to be US dollars 3.51/kg H2. The best option for low-cost power generation, using renewable resources such as photovoltaic (PV) devices, wind turbines, as well as electricity from the grid was assessed, considering the location of the case considered. All calculations were based on assumption of existing cable from the grid to the offshore, meaning that the cost of cables and distribution infrastructure were not considered. Models were created using HOMER Pro (Hybrid Optimisation of Multiple Energy Resources) software to optimise the microgrids and the distributed energy resources, under the assumption of a nominal discount rate, inflation rate, project lifetime, and CO2 tax in Norway. Eight different scenarios were examined using HOMER Pro, and the main findings being as follows: The cost of producing water with quality required by the electrolyser is low, compared with the cost of electricity for operation of the electrolyser, and therefore has little effect on the total cost of hydrogen production (less than 1%). The optimal solution was shown to be electricity from the grid, which has the lowest levelised cost of energy (LCOE) of the options considered. The hydrogen production cost using electricity from the grid was about US dollars 5/kg H2. Grid based electricity resulted in the lowest hydrogen production cost, even when costs for CO2 emissions in Norway, that will start to apply in 2025 was considered, being approximately US dollars 7.7/kg H2. From economical point of view, wind energy was found to be a more economical than solar.publishedVersio

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.publishedVersio

A comparative analysis of nature-inspired optimization approaches to 2d geometric modelling for turbomachinery applications

A vast variety of population-based optimization techniques have been formulated in recent years for use in different engineering applications, most of which are inspired by natural processes taking place in our environment. However, the mathematical and statistical analysis of these algorithms is still lacking. This paper addresses a comparative performance analysis on some of the most important nature-inspired optimization algorithms with a different basis for the complex high-dimensional curve/surface fitting problems. As a case study, the point cloud of an in-hand gas turbine compressor blade measured by touch trigger probes is optimally fitted using B-spline curves. In order to determine the optimum number/location of a set of Bezier/NURBS control points for all segments of the airfoil profiles, five dissimilar population-based evolutionary and swarm optimization techniques are employed. To comprehensively peruse and to fairly compare the obtained results, parametric and nonparametric statistical evaluations as the mathematical study are presented before designing an experiment. Results illuminate a number of advantages/disadvantages of each optimization method for such complex geometries’ parameterization from several different points of view. In terms of application, the final appropriate parametric representation of geometries is an essential, significant component of aerodynamic profile optimization processes as well as reverse engineering purposes